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Energy Economics
j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / e n e c o
An integrated approach to energy prospects for North America and the rest
of the world
Andrea M. Bassi a,b,⁎, Robert Powers c, William Schoenberg c
a
b
c
Millennium Institute, 2111 Wilson Blvd, Suite 700, Arlington, VA 22201, USA
University of Bergen, Postboks 7800, 5020 Bergen, Norway
SUNY-ESF, 1 Forestry Drive, Syracuse, NY, USA
a r t i c l e
i n f o
Article history:
Received 9 January 2009
Received in revised form 13 April 2009
Accepted 18 April 2009
Available online xxxx
Keywords:
Energy policy
Integrated modeling
Scenario analysis
Renewable energy
Vehicle efficiency
Oil supply
a b s t r a c t
Many international organizations and research institutions have released recently unequivocal scenarios on
energy's future prospects. The peak in global oil production is likely to happen in the next ten to fifteen years,
if it hasn't already happened, and decisions to be made in the near future are likely to have large impacts on
our quality of life in the coming decades. This study presents an integrated tool for national energy planning
customized to North America. The authors analyzed the impact of world oil production on economic, social
and environmental indicators. Two cases of global ultimate recoverable oil reserves are considered, a low and
medium estimate within current research. Three sets of policy directions were chosen: Business As Usual
(Market Based), Maximum Push for Renewables, and Low Carbon Emissions. Results of the simulations show
that without restrictions on emissions coal becomes the dominant energy in the longer term. On the other
hand, if US policymakers are able to effectively implement the necessary polices, such as a 20% RPS by 2020
and increased CAFE Standards, along with increased energy conservation and efficiency, the medium to
longer-term economic impacts of a global peak in oil production can be mitigated, while a sustained
reduction in emissions would require a larger effort.
© 2009 Elsevier B.V. All rights reserved.
1. Introduction
Energy is the foundation of every aspect of our “Global Economy”
(Hall et al., 1986). Without adequate actions aimed at preserving
energy availability, the well-being of our increasingly urbanized,
industrialized and growing world population faces the prospects of a
host of severe issues including: a reduced standard of living, declining
access to food (Pimentel, 2008) and clean water supplies (Gleick et al.,
2006), and the contraction of global trade and GDP (IPCC, 2007a,b,c).
In the next decade and beyond, energy-related decisions will be
made and polices enacted at national levels that will have direct
consequences for large segments of the human population and for the
global environment as a whole. These decisions, such as those to be
taken at the upcoming 2009 UN Summit on the Climate Change
Convention (UNFCCC—COP15), will directly and indirectly impact
energy and resource availability, human well-being, and even the
survival of the environment as we know it, on which all economies
ultimately depend. Among other organizations, the Association for the
Study of Peak Oil and Gas (ASPO) believes that the consequences of a
peak and subsequent decline in conventional global oil production may
⁎ Corresponding author. Millennium Institute, 2111 Wilson Blvd, Suite 700, Arlington,
VA 22201, USA. Tel.: +1 571 721 8275; fax: +1 703 841 0050.
E-mail address: [email protected] (A.M. Bassi).
cause unexpected and dire consequences for our society, economy, and
environment.
Many energy studies focus on how to best meet the increasing future
energy demand with available energy, and generally this approach
optimizes energy flows while minimizing costs (e.g. MARKAL (Fishbone
et al.,1983; Loulou et al., 2004), MESSAGE (Messner et al.,1996; Messner
and Strubegger, 1995), POLES (CNRS, 2006), and PRIMES family of
models (NTUA, 2005, 2006a,b)). Fewer studies investigate oil depletion
in isolation (Sterman, 1981; Sterman et al., 1988) and energy-economy
interconnections (Fiddaman, 1997; Bassi, 2008). Others study the net
energy gain of various energy sources (Hall et al., 1986; Cleveland et al.,
1984; Odum,1971). The American chapter of ASPO (ASPO-USA) believes
that all of these energy-related factors should be included in an
integrated simulation model.
Unfortunately, very few studies investigate explicitly and comprehensibly how the energy sector connects to society, the economy, and
the environment. Not may people are used to thinking in terms of
systems, which includes looking at the causal relations and feedbacks
existing among the above mentioned sectors. We aim here to apply
systems thinking to energy issues at the global, regional, and national
level.
Specifically ASPO suggests that we should look at the impact of
energy availability on the economy in terms of the productivity of capital
(e.g. by embedding energy as a component of the sector Cobb–Douglas
production functions), the availability of resources as a constraint for
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production, energy imports and their impacts on national accounts, as
well as the effects of energy expenditures on households accounts.
Another cultural issue is the relation of necessary investments in energy
acquisition to disposable income (Hall et al., in Pimentel, 2008).
The environment is also closely connected to energy. Water
availability is frequently an issue in energy production (e.g. tar
sands and biofuels), and air and water pollution are created when we
burn fossil fuels for energy, as well as when we build renewable
energy infrastructure. The International Energy Agency (IEA) in the
World Energy Outlook 2006 (IEA, 2007) concludes that in order to
keep emissions at current levels by 2030, we will have to have major
breakthroughs in the technologies associated with energy production
and consumption, all the while keeping costs low and affordable. The
IEA also points out that the options considered in their Beyond the
Alternative Policy Scenario (BAPS) simulation have no precedents in
history and should be developed quickly and taken to scale in order to
actively contribute at the global level.
Luckily many excellent detailed sector models exist, such as NEMS
(EIA, 2003), but comprehensive frameworks are either not available to
the public or discarded due to their complexity (e.g. black boxes)
(Bunn and Larsen, 1997). ASPO-USA aims at putting these studies
together to create an open-source, transparent, and comprehensive
model which:
– Raises awareness of fossil-fuel depletion, its implications for global
policy makers, and its impacts on the general public.
– Analyzes and quantifies the consequences of fossil-fuel depletion
on society, economies, and environment.
– Highlights new market opportunities in the energy field.
– Helps us investigate and understand the impacts of different
policies (e.g. rates of adoption for various mixes of renewable/
fossil/nuclear sources) under different future scenarios.
In order to involve a broad range of stakeholders and stimulate
discussion on the above, ASPO-USA decided to adopt an approach that
facilitates participation and consensus building by encouraging open
discussion with various stakeholders to test their assumptions and
draw conclusions about energy issues. This process will facilitate
strategy and policy development by simulating possible impacts of
alternative policy choices and strategic options.
In order to reach these objectives, the model should have the
following characteristics:
1. Accessibility: the model is open source and is available to interested
parties via the Internet for running remotely or for downloading,
and there is no requirement to invest in expensive or proprietary
software to run the model.
2. Transparency: the user interface permits clear visibility into, and
understanding of, the underlying components of the model, their
relations to each other, and to the model's database.
Therefore, the purpose of the modeling effort is to identify what
the main feedback loops underlying the system are and to generate
forward-looking scenarios of energy use at the world, regional, and
national levels, in which various policy decisions may be made that
affect the outcome of the scenarios generated. Ultimately, the model
should be a credible and useful policy-development tool that will be
available to decision-makers in governmental and business arenas, to
other non-profit organizations, and to members of the public. These
and other users not be experts at complex systems modeling, but it
will help them better understand the consequences and implications
of energy-related decisions and policy directions.
This paper focuses on analyzing energy issues and their consequences on the economies, societies, and environment of North
America (USA, Canada and Mexico). The project investigators have
developed and applied a System Dynamics-based model, which uses a
set of differential equations based on the best available sector models
as its mathematical foundation. T21-North America (T21-NA) is based
on the experience gathered by the Millennium Institute building a
number of customized T21 models over the last 25 years, and by the
State University of New York, College of Environmental Science and
Forestry (SUNY-ESF), whose faculty have produced some of the best
research in the field of net energy and systems ecology. The model is
built up on a set of causal relations that eventually create feedback
loops among different modules and sectors. These relations are based
on established laws of physics, thermodynamics and on economic
theories. Also they are based on observed social and political relations
to the largest extent possible. The model incorporates the best sectorstudies available to date into a one single modeling framework, and it
is calibrated using best-available data to ensure it reflects real-world
conditions accurately (see the section “Features and structure of the
model” for more details).
The results are disseminated to stakeholders through a user
interface specifically developed for the project. It allows users to
examine the model and interpret the results in several ways,
depending on their preferred learning style. The full model can be
viewed and explored, allowing users to see the key relations through
the equations used or through schematic causal tracing trees. Specific
feedback loops and sectors have their own visualizations with graphs
of their behavior over time, which is frequently much easier to
understand for lay-persons then the model sketches in Vensim.
Custom simulations can also be run through the interface and
compared to the simulations created by the project investigators.
Complexity is not a goal of the project. ASPO-USA and the two
main investigators believe that the model should incorporate
complexity only to the extent that it improves the reliability, accuracy,
and credibility of the model's results. It is our belief that overly
complex models tend to confuse the subject and cloud the understanding of the systems that they strive to represent, in particular the
relationship of the system represented to the rest of the global system.
Therefore, the principal investigators have spent much time and effort
ensuring that the model is as simple and as straightforward as reality
allows it to be, while preserving the key interlinkages.
2. Research questions
This study aims to analyze various energy-related issues in the
context of an integrated model that incorporates the relations of the
energy sector to the broader economic, social, and environmental
framework. The main research questions for this project are:
• What are the likely results of continuing current energy use on the
availability of conventional energy sources and on the rest of the
economy?
• What are the possibilities of expanding the use of non-traditional
fossil fuels (e.g. tar sands or shale oil) to meet liquid fuel needs?
• What are the net-energy consequences associated with a variety of
probable mixes of energy sources (i.e. conventional fossil fuels, nontraditional fossil fuels, biofuels, nuclear, and renewable, for example)?
• How would different alternative energy sources affect other aspects
of overall economic, social, and environmental conditions, such as
the impact of different biofuels on food production, water availability, and deforestation?
• What would be the amounts of GHG emissions under different
assumptions and approaches; will sequestration programs be
effective and what are their direct and indirect effects and costs;
and what effect control of such emissions would have on energy
availability?
• To what extent will endogenous factors, such as rising energy prices
and increasing scarcity, create enough incentives to support a shift
to sustainable/renewable energy, how will currently discussed
energy policies (e.g. CAFE and RPS) help the transition, and how
much exogenous political action is needed to achieve a transition to
a sustainable future? Indeed, is a sustainable future possible?
Please cite this article as: Bassi, A.M., et al., An integrated approach to energy prospects for North America and the rest of the world, Energy
Economics (2009), doi:10.1016/j.eneco.2009.04.005
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Among other research questions and energy-related issues, the
economical consequences of an early petroleum production peak and
energy return on investment will be addressed in more detail. In
considering these and other possibilities, the model generates
scenarios that show the results across all the key indicators for the
economy, society, and environment, so users can get a full picture over
a long time frame of the likely results, both positive and negative.
Petroleum is especially important among energy sources because
of the magnitude of its current use, it is extremely energy dense and
easily transportable (Cleveland, 2005), and its future supply is
worrisome (Campbell and Laherrère, 1998). The key issue is not
when we will run out of oil, but rather when demand will seriously
outstrip supply for biophysical reasons. Without a massive worldwide
recession, demand will continue to increase as human populations,
petroleum-based agriculture, and economies all continue to grow
(IEA, 2007). The production of oil and gas has been growing on
average several percent a year since the early 1900s, but since there
are finite (on a time scale relevant to society) reserves this cannot continue indefinitely (Campbell and Laherrère, 1998; Heinberg,
2003). The point of maximum production of an oil field or region, such
as a country or the world, is known as peak oil. It is important to note
that this isn't simply a theoretical concept, it actually occurred in the
United States in 1970 as well as in some 60 (of 96) other oil-producing
nations (Hubbert, 1974; Strahan, 2007). Several prominent geologists
have suggested that it may have occurred already for the world,
although that is not yet clear (e.g. Deffeyes et al., 2005; EIA, 2007a,b;
IEA, 2007).
Since markets have to balance, oil production plus the use of
inventories (collectively, supply) will always have to be equal to
demand. If potential demand grows while supply remains constant, or
even shrinks, prices will increase enough to bring the actual demand
down to the level of the available supply. While higher prices may lead
to some increases in supply, the biophysical realities of peak oil mean
that past a certain point, no matter the prices, production cannot be
increased. At this point we will enter the second half of the age of oil
(Vidal, 2005). The first half was one of year by year growth, the second
half will be of continued importance but year by year decline in
supply, with possibly an “undulating plateau” at the top. Natural gas
may help ease the tradition and buffer the impacts for a decade or so.
When the decline in global oil production begins, we will see the “end
of cheap oil” and a very different economic climate that can no longer
assume continuing access to cheap energy (Hall et al., in Pimentel,
2008).
An equally important issue is that of energy return on investment
(EROI), a concept born from physics (Hall et al., 1986). It is the energy
returned from an activity compared to the energy invested in that
process (Cleveland, 2005). The basic equation is:
Energy gained from an activity
EROI ¼
Energy used in that activity
EROI represents the ability of energy to do useful work, quantifying
the amount of energy available to do work by creating a ratio that
represents the amount of energy that a body has to do work relative to
the amount of energy it produces. This means that if the EROI of a
theoretical economy's fuel source is 20:1 for every 100 units of energy
brought into that economy 5 had to be invested to produce that 100.
Therefore, the net amount of energy available for other productive
uses is not 100 units, but rather 95 units. EROI takes into account the
concept of net energy and the ability of a fuel source to produce
surplus energy, which allows society and the economy to exist and
grow (Hall et al., 1986; Cleveland et al., 1984).
EROI should not be confused with conversion efficiency, which is
the efficiency with which one fuel is transformed or upgraded to
another. However, losses associated with these transformations are
included in the EROI calculation. Finally, the denominator for EROI is
3
usually calculated from the perspective of energy that is already
delivered, or readily deliverable, to society that is then used to get the
new energy. This is what differentiates EROI from exergy (Odum,
1983), which also looks at the work done by biological systems. For
example, accessing new oil reserves may require energy used
previously in a steel mill to make pipes or bits, and hence that is
energy that has already been delivered to society. Likewise oil is
usually pumped from the ground by burning natural gas to generate
electricity to run pumps. That gas (or the electricity) can usually be
transferred to the rest of society very readily, but has instead been
diverted to get the oil. So we would consider both of these costs as
existing energy that has been diverted from society and include them
in the EROI calculation.
3. Features and structure of the model
Various models can plausibly be used to generate the analysis
needed for the investigation of the impacts of the transition through
the oil peak, but not all energy models are appropriate. Indeed,
together with the necessity of including models that generate data at a
proper level of aggregation and within the selected boundaries, some
crucial characteristics should be attentively considered.
First, it is recommended to avoid relying exclusively on historical
correlations. Though we can learn a lot from them when building the
model and analyzing the results, they could be altered in the future by
nonlinearities and constraints. In other words, the past may not be
anything like the future. Therefore it is too risky to construct a model
about the future based totally on the past and the common linear
relations. A better way to construct this model would be to use structural
relations that replicate the causal structure of the processes modeled
and represent physical delays (e.g. time required to develop an oil field)
and other non-linearities related to the transition explicitly. Second,
given the imperfect information, uncertainty, and distributed decisionmaking peculiar to the energy and economy systems, it would be wise to
add to the modeling framework behavioral components, which
represent the information available to actors and the procedures they
use to process it and formulate decisions. If the model is to respond to
changes in the environment in the same way that real actors do, this
bounded rationality should be incorporated (Morecroft, 1983; Simon,
1979). Finally, the behavior of the model should be generated, so as to
consider changes in any variable of the areas of investigation considered,
account for feedback effects, and produce consistent results.
In addition to these general considerations, a model linking energy to
the economy, society and the environment that will be used in medium
and long-term policy planning should include at least the following
specific features (and their causes) as endogenous components:
1. Population: The total population of a country helps determine
important social, economic and environmental indicators. Income,
educational levels, and access to water, electricity, and many
additional interconnected factors all define population dynamics.
As one of the primary causes of energy demand, population should
be modeled explicitly. We are also interested in per capita energy
resources.
2. GDP: A healthy economy can support investments in various sectors
and provide the means to afford energy or the transition investments
needed. Households have to be able to sustain their standards of
living while supporting the Government through taxation, stimulating the economy through consumption, and investing in promising
industries. Since national policies drain resources from the main
actors in the economy (Government, households, firms, rest of the
world) and energy prices have a negative impact on it, an explicit
macroeconomic representation is needed.
3. Technology: Among others, energy efficiency (demand side) and the
ultimate recoverable resource (supply side) depend significantly on
technological development. As for the latter, usually only 40 to 50%
Please cite this article as: Bassi, A.M., et al., An integrated approach to energy prospects for North America and the rest of the world, Energy
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(depending on the geological characteristics of the reservoirs) of oilin-place can be recovered economically with current technology, but
the fraction recoverable has been rising and may rise substantially in
the future if technology continues to improve, but higher recovery
rates, up to the ultimate limit of just below 100%, are likely to become
more expensive.
4. Energy prices: Because energy prices have a strong influence on
energy demand and on the incentives for exploration and
development of fossil fuels and technological improvement for
renewable energy, it must be modeled explicitly. The effects of
production costs, supply and demand, and market imperfections,
should be incorporated.
5. Energy demand: Energy demand is sensitive to price. As prices rise,
the demand for energy will be depressed, and the production of
substitutes (“back-stops”, Nordhaus,1973) such as biofuels and heavy
oil will be stimulated. The pattern of demand and substitution will
have a strong influence on production and investment in exploration
and development of fossil fuels too. Delays in the response to changes
in demand and in the development of the substitutes industry should
be explicit.
6. Non-renewable resources depletion: The total quantity of fossil
fuels-in-place is finite. As it is consumed, the quantity remaining
inevitably declines, and the marginal cost increases, ceteris paribus.
Though improving technology may occasionally offset depletion
and cause the real price of oil to decline in certain periods of time,
the finite resource base and its depletion must be treated explicitly.
The table below contains spheres and sectors of T21-NA. There are
several linkages among the economic, social, and environmental
spheres and energy is the core to each one. Within each sphere are
sectors and modules, and structural relations that interact with each
other and with sectors and modules in the other spheres. None of the
spheres function independently, though different versions of T21 may
focus more heavily on one or the other of the spheres, depending on
the issues being addressed (Table 1).
A brief overview of the structure of T21-North America follows. A
fuller description (module by module and variable by variable) of the
model can be found in Modeling U.S. Energy with Threshold 21 (T21)
(Bassi, 2008).
The Economy sphere of T21-NA contains major production sectors
(agriculture, industry and services), which are characterized by Cobb–
Douglas production functions with inputs of resources (including
energy), labor, capital, and technology. A Social Accounting Matrix
(SAM) is used to elaborate the economic flows and balance supply and
demand in each of the sectors. Demand is based on population and per
capita income and distributed among the production sectors using
Table 1
Spheres and sectors of T21-North America.
T21-NA—spheres and sectors
Society and Economy
Energy and Environment
Population sector, 3 modules
Labor sector, 2 modules
Poverty sector, 1 module
Land sector, 1 module
Sectoral energy demand sector, 6 modules
Energy demand and trade sector, 9 modules
Energy supply sector, 16 modules
Energy prices and costs sector, 4 modules
Energy investments and capital sector, 5 modules
Energy expenditure, 2 modules
Energy technology sector, 1 module
Emissions and climate change sector, 4 modules
Production sector, 4 modules
Households sector, 1 module
Government sector, 5 modules
Investment sector, 2 modules
Trust funds sector, 2 modules
Technology sector, 1 module
ROW sector, 2 modules
Rest of the World (ROW) production sector,
4 modules
ROW price and cost sector, 3 modules
ROW emissions sector, 4 modules
Canada and Mexico demand and supply sector,
6 modules
China and India energy demand sector, 2 modules
Engle's Curves. This helps calculate relative prices, which are the basis
for allocating investment among the sectors. The government sector
collects taxes based on economic activity and allocates expenditures
by major category, which then impacts the delivery of public services,
subject to budget balance constraints. Standard IMF and BEA budget
categories are employed, and key macro balances are incorporated
into the model. The Rest of the World sector comprises trade, current
account transactions, and capital flows (including debt management).
Overall oil and natural gas production, consumption, and the resulting
export surplus in included to estimate oil and gas import potential for
North America. Income distribution and poverty levels are calculated
for the United States.
The Social sphere contains detailed population dynamics by sex
and one-year age cohort, health and education programs, and other
challenges, such as poverty levels. These sectors take into account, for
example, the interactions of family planning, health care and adult
literacy on fertility and life expectancy, which in turn determines
population growth. Population determines the labor force, which
shapes employment. Education, health levels, and other factors
influence labor productivity. Employment and labor productivity
affect the levels of production from a given capital stock. And these
factors all affect the levels of saving for investment and consumption
expenditures.
The Environment sphere tracks pollution and other impacts on the
environment from production and social activity, and their impact on
health, climate change, agricultural production, etc. It estimates the
consumption of natural resources—both renewable and non-renewable—and can estimate the impact of the depletion of these resources
on production or other factors. In addition, the Environment sphere
examines the effects of erosion and other forms of environmental
degradation on other sectors, such as agricultural productivity. Energy
demand, supply and trade, and pricing and investment are calculated
endogenously. Carbon cycling and climate change are included to
represent interactions among energy–environment–economy–
society components in a more comprehensive way.
The energy sources considered in the model are oil and direct
liquid substitutes (including heavy and extra heavy oil and ethanol
from corn and sugar cane), natural gas, coal, and electricity (generated
from nuclear power, renewable resources—wind, solar, geothermaland hydroelectric power). Energy modules include:
– Energy demand is disaggregated into residential, commercial,
industrial and transportation sectors for the US, demand for the
energy sources listed above is based on initial energy GDP
intensiveness, technology, energy prices and substitution among
energy sources. Demand affects, among others, energy production,
trade, prices and investments.
– Energy supply includes oil, natural gas and coal as primary sources.
Electricity is obtained from nuclear power and renewable energy.
Energy supply is calculated based on demand, availability of
resources (for fossil fuels), capital and exogenous policy interventions. The McKelvey Box (USGS, 1976) is used to define and classify
resources, which relate to the energy intensity of resource
extraction. Supply impacts, among others, consumption, prices,
trade and generation of pollutant emissions.
– Energy prices and costs cover oil, gas, coal, nuclear, electricity. Fossil
fuels prices are based on reserve and resource availability over the
medium and longer term; electricity price is calculated based on
the weighted cost of the energy sources utilized to produce it.
Energy prices from renewable resources are treated as exogenous
inputs and are assumed to decrease by 15% by 2050. Energy prices
and costs influence demand, investment, and production in the
energy sector, as well as production in the economic sectors.
– Energy investment is endogenous for oil, gas, and coal, and
exogenous for renewable and nuclear. Investment is based on
market profitability (both per each energy source separately and
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Fig. 1. Estimates of world ultimate recoverable petroleum (Source AAPG 2006) and simulated URR (T21-NA).
the whole market), technology, and production (which indirectly
takes into account the effect of resources availability and demand).
Investment directly impacts energy source production capacity
and technology improvement.
– Energy technology addresses energy efficiency (for the four demand
sectors), exploration, development and recovery (for fossil fuels,
separately), and vehicle fuel economy. Energy technology is
calculated based on investment and energy prices. It affects
resource availability and production (in the case of fossil fuels,
through exploration, development and discovery), demand, prices
(indirectly), and investment (through the average energy technology available).
– Pollution includes emissions (CO2, CH4, N2O, SOx, and total
greenhouse gasses). Pollution is based on fossil fuel consumption
and technology levels; it affects carbon cycle and climate change,
as well as life expectancy.
The modules above calculate energy demand for the US, Canada,
Mexico, China, India and the rest of the World, and energy supply and
emissions for North America and the rest of the World.
4. Definition of the scenarios
A set of assumptions can be simulated with T21-NA to create
various scenarios on top of which different policies can be tested. This
study was largely carried out before the current economic crisis, but
insights emerging from the analysis can be useful to analyze and
understand recent events and may shed light on the path to recovery.
For this exercise, following the suggestions of ASPO-USA, assumptions
are limited to the total ultimate recoverable resource (URR) and
scenarios analyzed the implementation of a variety of polices.
ASPO-USA believes that the United States Geological Survey
(USGS) low and medium estimations of the URR (USGS, 2000) for
conventional oil and natural gas liquids are reasonably on target,
considering that one of the biggest exponents of ASPO, Colin Campbell
and Jean Laherrère, have estimated the URR to 1.9 trillion barrels for
crude oil only (Campbell and Laherrère, 1998). The figure below
shows URR estimations from 1945 until 2003. The two dots in 2005
represent the assumptions simulated in the Medium and Low URR
scenarios. The red line shows the amount of recoverable reserves as
simulated by T21, starting from 1980. In the Low URR case, technology
will not allow to recover a lot more oil than we currently expect.
Conversely, in the Medium URR case, new discoveries and technology
will allow for the recovery of more reserves. Users simulating T21 can
decide how much oil reserves and resources are in place at any point
in time. For simplicity we analyze three sets of scenarios: one based on
the USGS Low 2.2 trillion barrels-, and two based on the USGS Medium
Estimate—3 Trillion barrels—(where peak oil takes place in 2020 and
where a plateau phase takes place in 2011 and production declines
after 2020) (Fig. 1).
The policy choices of T21-NA range across energy, society,
economy, and the environment. Taxes on gasoline or income, as well
as the introduction of commercially viable breakthrough technology
can be tested with the model while simulating the impact of improved
Corporate Average Fuel Economy (CAFE) standards or the approval of
a Federal Renewable Portfolio Standard (RPS). This paper analyzes
three main groups of policy/action options in the context of both the
low and medium URR sets of energy availability assumptions: Market
Based, Maximum Push for Renewables, and Low Carbon Emissions.
The Market Based simulations serve as the Reference Scenario
proposed by ASPO-USA. It is based on a market economy, where
(1) Federal laws do not regulate electricity production from renewable
energy sources, (2) there is no restriction on CO2 emissions, and (3) heavy
subsidies for ethanol are allocated as proposed by the United States
Department of Agriculture (USDA) until 2016 (USDA, 2007). To simulate
the introduction of renewables into the market, we use the EIA reference
case for renewable energy production, while ethanol production,
although endogenously calculated, follows USDA projections.
The Maximum Push for Renewables scenario simulates what would
happen if there were large support for bringing renewable energy on
line by the Federal Government. It is therefore assumed that a
Renewable Portfolio Standard (RPS) of 20% by 2020 is approved by the
Congress, as proposed by H.R. 969, that there are still no restrictions on
CO2 emissions, and that subsidies for ethanol production are retained.
The outlook of the American Council on Renewable Energy (ACORE) is
also tested in another scenario in this set. In their Outlook on
Renewable Energy in America (ACORE, 2007), non-profit and
academic organizations, trade associations, and governmental agencies provide estimations of potential energy production capacity from
various renewable energy sources. They state that a total of 635 GW
(GW, equal to 1 billion watts) of renewable power capacity can be
added to the existing 99 GW by 2025 (ACORE, 2007).
The Low Carbon Emissions scenarios add additional policies on top of
the implementation of the 20% RPS: the CAFE Standards will be increased
(H.R. 1506 by Rep. Markey) in Emissions Low and Med. And there will be
increased electrification of light urban, commuter, and freight rail in the
Emissions Trans scenarios. The former assumes that, as proposed by H.R.
1506, new standards for passenger vehicles b10,000 lbs. will be set at
35 mpg by 2018, followed by a 4% increase each year thereafter. The latter
originates from conversations with transportation expert Alan Drake and
luminary Ed Tennyson, who believe that by following the example of
European countries such as France and Germany, the US can add the
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Fig. 2. Comparison of average US energy price (in real USD/Million Btu) simulated by T21-NA under different scenarios.
equivalent of a NYC subway per year in the US starting in 2011 (both for
urban rail, commuter and freight transportation).
5. Results
The main differences among the scenarios simulated, which
include the assumption that the peak in world oil production will
take place in 2011, can be summarized as follows. Energy prices are
among the main factors affecting GDP, therefore when oil production
turns downwards in 2011 at 29.5 Mb/year (Low URR scenarios), real oil
prices jump to $285 per barrel (in year 2000 dollars) while GDP
declines by 9% in all Low URR scenarios. High prices and falling GDP
drive a reduction in energy demand (− 5%), which makes oil prices
decline to $190 in 2013. Furthermore, this factor, as observed in 1983
and 1984, allows a less energy intensive economy, where energy
conservation has taken place, to grow until energy prices start
increasing again. In fact, the GDP growth rate turns positive in 2014
and oscillates around zero until the energy transition is fully completed
by 2025.
On the other hand, it has to be noted that high energy prices
reduce discretionary consumption, which, as shown by the Cheese
Slicer later in the paper, can be seen as an indication that quality of
life is decreasing.
Over the longer term, though demand is rapidly decreasing
following declining supply, oil prices will keep increasing due to the
higher cost of extracting oil from the less accessible reservoirs that will
become a larger portion of the supply base, reaching $300 in 2050. In
fact, the energy return on investment for oil and gas is projected to
decline, reaching a ratio lower than 10:1 in 2050 for economically
produceable wells.
A push towards renewables and substitution for oil (Renewable
and Emissions scenarios), allows the economy to reduce its dependence on expensive energy only in the medium to longer term, given
the delay in capacity building. As a consequence, the average energy
price declines and is constantly lower than in the Market Based
scenario (by about 18%) after 2020 and throughout the simulation
(Figs. 2 and 3).
It has to be noted that when simulating the Renewable (ACORE)
and Emissions scenarios, electricity generation from renewable
energy sources grows considerably. As a consequence, the average
cost of electricity increases (+40% with respect to the Market Based
scenario), especially when both ACORE and electrification of rail are
assumed to take place. Nevertheless, the high price to pay for
electricity is generally offset by the savings generated by a reduced
consumption of oil and more expensive fossil fuels, and both
households and GDP profit from it. An increase in energy prices
with respect to the current level though, will shrink resources for
Fig. 3. Comparison of real GDP growth rate simulated by T21-NA under different scenarios and historical data.
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households and the industry. Also, support to the government is
needed to contain debt. For the US it is assumed that taxation
increases (30% of GDP is to be taxed in 2050 in the Low URR scenario
and about 26% in the Medium URR case) to allow the Federal
Government to avoid the negative spiral of debt and interest rates and
keep foreign capital at about 30% of total national investment. The
debt GDP ratio is assumed to be maximum three times as much as
current level.
When simulating the USGS Medium URR estimation, GDP will
grow at a lower rate than Congressional Budget Office (CBO) and
Energy Information Administration (EIA) projections in the Market
Based and Renewable scenarios, but the growth rate is still higher rate
than in the Low URR case. This is due mainly to the fact that with
larger simulated reserves peak oil and the energy transition are
pushed back to 2020, and by then the economy will be less energy
intensive and less sensitive to energy prices due to increased
efficiency. Furthermore, the additional capacity and production of
unconventional oil and biofuels will help ease the energy transition,
leading to a 15% larger GDP in 2050, due to about + 0.3% in growth
rate throughout the simulation. As previously stated, GDP grows faster
in the Renewable and Emissions scenarios than in the Market Based
case, but their contribution is smaller than in the Low URR case, where
the economy is more sensitive to energy prices.
The behavior description and analysis of T21-NA concentrates on
energy and its interconnections with the three other sectors: society,
economy, and the environment. While the USA part of the model is
more detailed in its representation of energy, society, economy and
environment; the models of Canada, Mexico and the rest of the world
components are mainly focused on energy.
5.1. Society
Total population in the USA is projected to grow by between 32.6%
(Market Based Low URR) and 38% (Low Emissions High URR) in the
period 2006–2050, reaching 402.5 or 416.7 million people. These
figures are in line with the projections from United Nations Population
Division. Population growth in the US, especially for the elderly age
cohorts, is likely to affect the sustainability of social security and
medicare trust funds as indicated by the increase in their share of the
population in the breakdown by age cohorts.
By looking at the population pyramid, it is clear that the population
groups aged 65 and older will increase faster than the average total
population. In particular, two population waves are evident in the
medium term; one of which is the “baby boomer” group which
contributes to the growth of the elderly population.
7
Employment is projected to remain about constant through 2050
in the Low URR scenario (140 M) and increase in the Medium URR
case (211 M). Employment is sustained in the former simulation by an
increase in fuel prices and a reduction in labor cost, which stimulates
the shift back towards a more labor intensive economy.
5.2. Economy
The main components of the Economic sector included in T21-USA
are related to the four agents acting in the USA economy: producers,
government, households, and the rest of the world (ROW) (Pyatt,
1991; Drud et al., 1986).
A few indicators are shown per each agent:
1. Producers: production (GDP) and its components (agriculture,
industry and services);
2. Government: revenues, expenditure, investment, debt, and trust funds;
3. Households: private investment, per capita disposable income and
consumption, and propensity to save;
4. ROW: balance of payments, trade balance, and net services.
Real GDP at market price in the Low URR case is projected to
remain at about its current level in 2050 ($13 trillion, using 2000 as
the constant dollar base year) and rise to $44 trillion in the Medium
URR scenarios. Even assuming oil field technology will improve when
the ratio between demand and supply is very tight, generating an
undulating plateau in oil production until 2020, this will not improve
the long-term trend of GDP much from the Low URR scenario. It has to
be noted that since both Renewable and Emissions scenarios reduce
US dependency on oil, GDP will perform better (+5% and + 24% in the
Low URR case and +13 and +16% in the Medium URR scenarios
respectively) (Figs. 4 and 5).
The elasticity of GDP to energy prices is assumed to be a function of
the overall energy intensity of the economy. This means that the
economy becomes less and less sensitive to energy prices as its
efficiency increases. Elasticity in 1980 is set to −0.3, the lowest value
among various estimations dating back to the highly volatile early
eighties (D. Gately, 2004; S. Brown, 2003), and reaches − 0.115 in
2050. Among sectors, agriculture is projected to suffer the least from
the increase in energy prices, not because of lower vulnerability of the
industry (in fact yield decreases due the increasing cost of energy
inputs in the Low URR case) but because of the need to produce
agricultural products for domestic consumption. Historical comparison is mainly made with data series published by the International
Monetary Fund (IMF) and the Bureau of Economic Analysis (BEA).
Fig. 4. Comparison of real GDP simulated by T21-NA under different scenarios and historical data.
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Fig. 5. Comparison of per capita real GDP simulated by T21-NA under different scenarios and historical data.
Nominal Federal government revenues and expenditures generally
follow the GDP trend, as the former are obtained though taxation and
the latter are allocated based on available revenues. As a consequence,
the overall fiscal balance (i.e. revenues minus expenditure) will
remain negative throughout the simulation ($−4 trillion and $−12
trillion in the Low and Medium URR respectively), and the continuing
deficit will lead to a steady increase in public debt (two or three times
higher than GDP in 2050 in the Low and Medium URR scenarios,
respectively) and likely also the share of government expenditures
allocated to debt service.
The Balance of Payments surplus is projected to grow slowly in the
medium and long term. The negative performance of the current
account (calculated as the sum of resources balance, net factor income
and net transfers) is offset by the growth in the capital and financial
accounts as foreign investment in USA government bonds and other
private sector assets will continue the current positive trends.
However, this means that the foreign level of USA assets will increase.
Per Capita GDP follows the behavior of GDP, and households
savings are reduced by the increasing energy cost and increasing
taxation, especially in the Low URR case. As shown in the figure above,
per capita GDP decreases in the base case, as population grows faster
than GDP. The projections of T21-NA also show that investing in
renewable energy and more efficient transportation reduces energy
expenses in the medium to longer term and makes income increase.
When actions are not taken timely and the transition beyond oil is
delayed (Plateau scenarios), extra measures are required to sustain
income growth, which turns negative before 2050.
5.3. Energy and environment
Total energy demand projections indicate very different trends for
the different scenarios. As for the Low URR, demand ranges between
71 and 86 QdBtu (Quadrillion British Thermal Units) in 2050,
declining after 2011. The Medium URR scenario shows that energy
demand increases until 2020 and then levels off due to peak oil and
increasing energy prices for a few years. Demand starts growing again
in 2032 due to GDP, which increases driven by the substitution for oil,
reaching 157 QdBtu in 2050 (Fig. 6).
A more interesting analysis regards consumption by energy source,
which towards 2050 shifts from oil (− 18%) and gas (−5%), to coal
(+10% in the Market Based scenarios), nuclear (+8% in the Low URR
scenarios) and renewables (+15% and 25% in the Emissions and
Renewable scenarios respectively).
Looking at oil in more detail, the dependency on foreign crude is
projected to increase from 65% in 2005 to 70% in 2011 and then decline
to 50% in the Low URR case by 2050, while it increases to 90% in the
Medium URR scenario over the same period of time. On the other
hand, both US and world demand rapidly decrease after production
Fig. 6. Comparison of total US energy demand (QdBtu/year) simulated by T21-NA under different scenarios and historical data.
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Fig. 7. Comparison of US oil demand (QdBtu/year) simulated by T21-NA under different scenarios and historical data.
starts declining and prices shoot up, respectively in 2011 and 2020 for
the Low and Medium URR scenarios (Fig. 7).
Fossil fuel demand from the rest of the world is going to increase
at a higher rate than in the US over the next 40 years, mainly due to
demand from fast growing countries such as China and India.
Specifically, simulated petroleum demand will more than double in
China (+ 224%) and India (+ 215%), by 2050. Canada will increase
its oil consumption by about 60% (Low URR) and 80% (Medium
URR) and Mexican consumption will be similar to current level,
mainly due to the effect of declining domestic oil production on
GDP. Despite increasing fossil fuel demand in Canada and Mexico,
production of oil, gas, and coal is projected to decline soon (apart
from tar sands that will increase throughout the simulation
reaching 1.5 Mbbl in 2050). As a consequence of increasing demand
and declining domestic supply, Mexico will become a net importer
of oil between 2018 and 2025, while Canada will stop exporting
natural gas around 2030 and coal around 2015. The strong economic
growth until mid 2008 and the downturn and the subsequent
decline in energy prices may both accelerate depletion of recoverable Canadian natural gas and Mexican oil. This is due to (1) high
production (driven by high profitability with an expanding
economy) and (2) lower investment (driven by higher price
competition during periods of declining demand and limited access
to capital). Both factors will contribute to the increased dependence
of the US on imports from the rest of the world.
While total World CO2 emissions are projected to increase throughout the simulation, with the only the exception of a few years following
peak oil, U.S. emissions decline in all Low URR scenarios by 2050
(reaching about 3.5 Billion Tons per year, −40% with respect to 2006
and well below 1990 levels) and increase in the Medium URR cases (to
8 Billion Tons per year, +33% with respect to current level), driven by
increasing GDP and energy demand (Fig. 8).
6. Special topics
6.1. EROI
T21-NA calculates EROI in two different ways: a conventional one
(Hall et al., 1986; Cleveland, 1992, 2005) with investments and
outputs defining the energy gain, and second one in which energy
inputs are a function of energy output and depletion. Results do not
vary across different scenarios, as EROI is calculated using longer-term
depletion trends, which are based on the total stock of fossil fuel
resource, reserve and cumulative production.
The first method used by T21-NA to calculate the Energy Return on
Investment (EROI) for petroleum (oil and gas) and coal is similar to
Fig. 8. Comparison of US GHG emissions (tons/year) simulated by T21-NA under different scenarios and historical data.
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the methods used by Hall and Cleveland (Hall et al., 1986; Cleveland,
1992). In this method we calculate Energy Input as the summation of
Direct and Indirect Energy inputs. Direct Energy inputs refer to the
fuels used in the mining and production process, while the Indirect
Energy inputs refers to the investments into capital to produce the
energy.
The EROI for oil and gas is given as a single value since the majority
of oil and gas are found together in the same fields (Hall et al., 1986;
Cleveland, 2005). Indirect energy inputs for oil and gas is a function of
oil and gas investment as calculated by T21, based on expected
demand, market profitability, and resource availability, converted to
an energy value (Energy Intensity). Direct energy Inputs are calculated
as a function of oil and gas depletion. The assumption here is that as
more resources are used, it will take proportionally more energy to
extract the remaining ones. Using this method for petroleum, we get a
result that is fairly consistent in the long term trend with that of
Cleveland. Our EROI starts at 25:1 in 1980 and declines rather steadily
to around 6:1 in 2050. Our results differ moderately from those of
Cleveland in the short terms paths because T21-NA aims at
representing medium to longer-term trends. By doing so, instead of
computing investment, we rather focus on capital in place, avoiding
short-term oscillations due to speculation and energy price volatility.
As a result, the long-term trends match up well with Cleveland's
study. More specifically, according to T21-NA the direct energy inputs
are constantly on the rise; starting off at 1.3 quad in 1980 and reaching
2.2 quads by 2050. The Indirect energy inputs oscillate between 0.3
and 0.4 quads at the beginning of the simulation, falling below 0.1
quads by 2050.
The only differences in the above method for coal are that we do
not disaggregate direct and indirect energy inputs, and we used an
energy per dollar conversion factor that uses industrial demand and
production only. The only reason for using a different energy
conversion factor is that recent research on coal EROI by ESF has
used the formulation above, and the authors decided to follow the
same method to ensure full compatibility and replicability of the
results. We did not disaggregate the energy inputs into coal mining
because domestically produced coal has not yet peaked and is not
projected to peak (in quantity terms) until after the run of this model.
This means that the energy it takes to find the coal and mine the coal is
not projected to undergo any significant changes, and it makes sense
to make the assumption that the direct energy inputs will always be
half of the indirect energy inputs because this is what we have
empirically observed in the US economic census data (Economic
Census, various years). The behavior of the coal EROI is very similar to
an updated version of Cleveland's study done by ESF recently. T21
undershoots the updated version of Cleveland's study by about 15%.
The model's Coal EROI starts out at around 60 in 1980 and rises to a
plateau of approximately 170 in 2015. This behavior is related directly
to the coal energy input because coal production remains relatively
stable throughout the time span simulated. Our coal energy input first
rises steadily from 1980 to the mid 1990s where it proceeds to plateau
at around.3 quads. From there it declines steadily to .15 quads in
approximately 2020 where it stays until 2050. The reason for the
overall decline in Energy Input is that the energy intensity in the coal
industry falls over time, due to increases in efficiency (IEA, 2007).
These results have limitations and should be viewed as a best case
scenario since we do not disaggregate into different coal mining
technology types and their future applications. Much of this EROI
formulation is based on empircal studies (Cleveland, 1992) and recent
historical data (Economic Census, various years) when about a quarter
of coal is produced from longwall mines, which are very energy and
labor efficient compared to continuous mining operations. In the
future, we cannot expect the same results from longwall mines due to
resource considerations.
The second method used in the model to calculate EROI for
petroleum still uses the same basic energy output over input formula,
but derives the inputs in a novel way. The energy inputs are initialized
by the share of the energy outputs in 1980, and are then driven by
depletion. This assumption has been made because as oil and gas
become more and more depleted, it will take more and more energy to
find and bring them to the surface, but still the effort for oil and gas
production is anchored to demand and therefore production, through
prices. In other words, at the beginning of production, technology and
investment are the major determinants of the production rate, but as
time progresses depletion becomes the dominant factor. The major
exogenous factor used is the initial share of energy input over output,
which was derived empirically from the Economic Census data and
Cleveland's studies (1992 and 2005). This method assumes a
theoretical EROI curve highly dependent on the amount of resources
in the ground. At first, when only a small percentage of the fuel has
been produced there is a very high EROI, because the high reservoir
pressure allows oil and gas to reach the surface and be produced with
very little additional energy investment. Then as more and more of the
fuel is produced the reservoir pressure drops off and the rate of
production eventually declines, unless technology (e.g. secondary and
tertiary recovery), which requires additional energy input, are used.
Because U.S. production peaked 10 years before our model begins,
the main driver of EROI for domestic petroleum is depletion, which is
precisely why we use it to determine the energy inputs of oil and gas
production. Our results using this method are similar to the results
above, the EROI ranges from 22:1 in 1980 and declines steadily to 13:1
in 2050. The energy input for this method is relatively stable around
1.8 quads until approximately 2005 when it begins to decline slowly to
1.1 quads in 2050. The reason for this late decline is that the energy
output of oil and gas falls steadily over time in conjunction with the
increasing rate of depletion. It should be noted that this EROI
calculation method is sensitive to the initial value of the energy
input. Nevertheless, whether we use Cleveland's or ESF's inputs, the
projection of EROI is consistent and matches the medium to longer
term trend of the respective reference studies. Overall, the two
methods show similar long-term trends: the EROI for oil and gas in the
US is declining steadily along with production.
7. The Cheese Slicer a conceptual environmental–economic–energy
model
The Cheese Slicer is a conceptual model, first realized in Pimentel
(2008), by Dr. C. Hall et al., that helps understand what might be the
most basic implications of the energy transition on the economic
activity of any energy importing country. The US case is presented
here and analyzed. The idea underlying of the Cheese Slicer is that the
economy requires energy to operate, and in the absence of energy the
economy drops. The second premise of this model is that the economy
is faced with decisions on how to allocate resources; not only to
maintain itself (e.g. perform necessary maintenance on old roads and
bridges), but to invest in its own future (money spent on medical
research, new roads etc.) so that it may have the potential to grow.
The figure below is a diagram of this model. The large central
square represents the economy where, among other things, energy,
natural resources, labor and capital, enter the economy, and value
added is generated in the form of GDP. In this exercise, the economy is
placed within the confines of the world because, like any other entity,
it is bound by the laws of nature (Hall et al., in Pimentel, 2008). The
arrow labeled “energy” represents the flow of raw unprocessed energy
into the economy, which will be upgraded and then used to produce
value added. Without this most basic flow, the present economy
would turn completely labor dependent and would produce very little
to no output (Hall et al., in Pimentel, 2008). The value added produced
by the economy, GDP, could be represented as either money or
embodied energy. Monetary values are selected for this study, and
further research will look at the energetic equivalent of all monetary
flows (Fig. 9).
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Fig. 9. Conceptual representation of the Cheese Slicer.
There are many paths a single output of the economy (one dollar)
can follow. These include investment or consumption, either for
necessity (non-discretionary) or for pleasure (discretionary). With
T21-NA, we are able to calculate both consumption and investment
and simulate them under different assumptions. An explanation of the
private sector follows. The Households Accounts module of T21
represents how various economic flows are combined to determine
household income, and how this income is split into consumption and
savings, part of which eventually becomes investment. For the sake of
simplification, in T21 we assume that all the value-added created by
Fig. 10. Cheese Slicer composition in 2007 and 2050.
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the economy is transferred to households, which pay all domestic taxes
and duties. In other words, we do not separately consider that part of
the value-added is retained by the firms, taxed by the government, or
eventually directly re-invested. Thus, the saving-investment behavior
of firms is assimilated to that of households. This assumption seems to
be acceptable, considering that households are in most cases the major
stakeholders in a firm's activities and have therefore a strong influence
on their saving-investment behavior.
Disposable income is calculated as total households revenue minus
the fiscal withdrawal of the government from households (the
government's budgetary revenue). Disposable income is then allocated
into consumption or savings according to interest rates and Engel's
law, which says that the propensity to save is positively related to the
level of per capita income. In addition, T21-NA accounts for the effect
of energy prices and interest rates as major factors affecting
consumption and investment decisions. Non-discretionary consumption is assumed to be a function of total population, while the three
categories of investment in the Cheese Slicer are calculated as capital
depreciation (maintenance), investment minus depreciation (new
investment), and energy investment (energy input).
As energy becomes more expensive, due to the mismatch between
demand and supply for oil or due to increasing production of
electricity from renewable sources, non-discretionary consumption
increases (from 39% to 50% of GDP in 2050, Low URR case) while
discretionary consumption and investment shrink (from 36% to 15%
and from 3% to zero in 2050, Low URR case). As for the latter, when
GDP grows slightly (Low URR Market Based scenario), maintenance
remains about constant, energy acquisition is pushed upwards from
10% to 22% by the net effect of decreasing energy return on
investment–positive- and declining energy demand –negative. Energy
input is higher in the Medium URR case due to depletion and a slower
energy transition.
The Cheese Slicer shows that the impact of the energy transition
beyond oil can have relevant impacts on both households and the
industry. The former will be affected by growing energy expenditure,
which will reduce discretionary consumption in spite of improved
energy efficiency and increasing energy conservation, each of which
has investment costs. The latter will have to allocate increasing
investments to energy in order to produce decreasing amounts of oil
and gas from almost fully depleted reservoirs. Nevertheless, more
research is required to fully represent the impact of alternative
portfolios of energy sources on the total energy input to the economy
(Fig. 10).
8. Conclusions
Many international organizations, think tanks, and research
institutions have released recently unequivocal scenarios on energy's
future prospects. The peak in global oil production is likely to happen
in the next ten to fifteen years, and decisions made between then and
now are likely to have large impacts on our quality of life in the coming
decades.
Although most of the discussion has been focused on how much oil
is left in the ground, the IPCC among others has stated that we are
facing an even bigger problem with global warming. While a larger
global URR may, on one hand, allow the economy to grow and to have
us more prepared for the upcoming energy transition, on the other
hand, it would both allow our economy to expand its rate of fossil fuel
consumption and delay North America's transition to alternative
sources of lower GHG producing energy, thereby increasing the flow of
emissions produced. In addition, this depletion of oil reserves is
accompanied by a decline in EROI, indicating an even steeper drop off
in the amount of energy oil can deliver to society.
The authors analyzed two cases of global ultimate recoverable oil
reserves: 2.2 and 3 trillion barrels, a low and medium estimate within
current research. Three sets of policy directions were chosen: Business
As Usual (Market Based), Maximum Push for Renewables, and Low
Carbon Emissions. Without restrictions on emissions, as in the Market
Based scenarios, coal becomes the dominant energy source and
substitutes for oil in the longer term. In all of the simulations,
government taxes are expected to rise to service increasing debt and
keep foreign capital at 30% of national investment. If US policymakers
are able to implement the necessary polices, such as a 20% RPS by
2020 and increased CAFE Standards, along with increased energy
conservation, we may have the opportunity to smooth the medium to
longer-term impacts of a global peak in oil production and an
increased reliance on coal. There is no silver bullet which will solve
our energy needs, but there is a solution that lies in developing a good
strong renewable energy system that minimizes GHG emissions along
with a program to reduce demand. It will require a number of effective
policies and actions.
The impact of the energy transition beyond oil can have relevant
impacts on both households and the industry, as shown by the Cheese
Slicer conceptual model that helps understand the dynamics of
consumption and investment allocation. Households will be affected
by growing energy expenditure, which will reduce discretionary
consumption in spite of improved energy efficiency and increasing
energy conservation. The industry will have to allocate increasing
investments in order to produce decreasing amounts of oil and gas
from almost fully depleted reservoirs and discretionary investments
will be reduced to zero. Nevertheless more research is required to fully
represent the impact of alternative portfolios of energy sources on the
total energy input to the economy.
The authors identify several directions for future research. The
most immediate is to expand the model to include additional
countries and model the energy trading interactions between them,
in addition to Mexico and Canada already implemented in the model
currently. This will imply the study of additional energy technologies
and the endogenous calculation of EROI for other energy sources. The
expansion of the model to create a game interface is being considered
to help users understand how some of the key feedback loops in the
real world interact with each other, and what policies and actions have
the best hope of shaping an acceptable future.
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